# Soft Label

Source: https://www.potatoannotator.com/docs/annotation-types/soft-label

The soft label annotation schema allows annotators to assign probability distributions across multiple categories rather than making a single hard classification decision. Annotators use sliders to distribute a fixed total (e.g., 100 points) across labels, capturing the degree of uncertainty or overlap between categories.

## Overview

Soft labeling is useful when items may partially belong to multiple categories. Instead of forcing annotators to choose one label, this schema lets them express relative confidence across all options. The sliders are linked so they always sum to the configured total, and an optional distribution chart provides visual feedback.

## Quick Start

```yaml
annotation_schemes:
  - annotation_type: soft_label
    name: sentiment_distribution
    description: Distribute 100 points across sentiment categories based on how much each applies.
    labels: ["Positive", "Neutral", "Negative"]
    total: 100
```

## Configuration Options

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `annotation_type` | string | Required | Must be `"soft_label"` |
| `name` | string | Required | Unique identifier for this schema |
| `description` | string | Required | Instructions displayed to annotators |
| `labels` | array | Required | List of category labels (minimum 2) |
| `total` | integer | `100` | The fixed sum that all sliders must add up to |
| `min_per_label` | integer | `0` | Minimum value each label must receive |
| `show_distribution_chart` | boolean | `true` | Display a pie or bar chart showing the current distribution |
| `label_requirement.required` | boolean | `false` | Whether the annotation must be completed before moving on |

## Examples

### Sentiment Distribution

```yaml
annotation_schemes:
  - annotation_type: soft_label
    name: sentiment_distribution
    description: How much does each sentiment apply to this text?
    labels: ["Positive", "Neutral", "Negative"]
    total: 100
    show_distribution_chart: true
```

### Emotion Intensity

```yaml
annotation_schemes:
  - annotation_type: soft_label
    name: emotion_mix
    description: Distribute points to reflect the mix of emotions in this utterance.
    labels: ["Joy", "Sadness", "Anger", "Fear", "Surprise", "Disgust"]
    total: 100
    min_per_label: 0
    show_distribution_chart: true
```

### Topic Relevance

```yaml
annotation_schemes:
  - annotation_type: soft_label
    name: topic_relevance
    description: How relevant is this document to each topic?
    labels: ["Politics", "Sports", "Technology", "Entertainment"]
    total: 100
    label_requirement:
      required: true
```

### Forced Minimum Allocation

```yaml
annotation_schemes:
  - annotation_type: soft_label
    name: genre_mix
    description: Allocate points across genres. Each genre must receive at least 5 points.
    labels: ["Rock", "Pop", "Jazz", "Classical", "Electronic"]
    total: 100
    min_per_label: 5
```

## Output Format

```json
{
  "sentiment_distribution": {
    "labels": {
      "Positive": 45,
      "Neutral": 30,
      "Negative": 25
    }
  }
}
```

Values always sum to the configured `total`.

## Best Practices

1. **Use when categories overlap** - soft labels are ideal when items genuinely belong to multiple categories to varying degrees
2. **Keep the label count manageable** - more than 6-7 labels makes the slider interface unwieldy
3. **Set a meaningful total** - 100 is intuitive as percentages, but smaller totals work for simpler tasks
4. **Use min_per_label sparingly** - forcing minimum allocations can bias results when a label truly does not apply
5. **Enable the distribution chart** - visual feedback helps annotators see their allocation at a glance

## Further Reading

- [Likert Scales](/docs/annotation-types/likert-scales) - Ordinal rating scales
- [Slider](/docs/annotation-types/slider) - Single continuous value annotation
- [Quality Control](/docs/features/quality-control) - Attention checks and gold standards

For implementation details, see the [source documentation](https://github.com/davidjurgens/potato/blob/main/docs/annotation_schemes.md).